By Topic

Blind MIMO identification using the second characteristic function

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Eidinger, E. ; Sch. of Electr. Eng., Tel-Aviv Univ., Israel ; Yeredor, A.

We propose a new approach for the blind identification of a multi-input-multi-output (MIMO) system. As a substitute to using "classical" high-order statistics (HOS) in the form of time-lagged joint cumulants, or polyspectra, we use the estimated Hessian matrices of the second joint generalized characteristic function of time-lagged observations, evaluated at several preselected "processing-points." These matrices admit straightforward consistent estimates, whose statistical stability can be finely tuned (by proper selection of the processing-points)-in contrast to classical HOS. Transforming the obtained matrix sequence into the frequency-domain, we obtain (and solve) a sequence of frequency-dependent joint diagonalization problems. This yields a set of estimated frequency response matrices, which are transformed back into the time domain after resolving frequency-dependent phase and permutation ambiguities. The performance of the proposed algorithm depends on the choice of processing-points, yet compares favorably with other algorithms, especially at moderate signal-to-noise ratio conditions, as we demonstrate in simulation results.

Published in:

Signal Processing, IEEE Transactions on  (Volume:53 ,  Issue: 11 )